Hello everyone, welcome to my blog.
My learning from introduction to data analytics with Dr. Francis Okoye. It has been very interesting; to understand, Data, Data cleaning, the benefits of data cleaning, and how data can be cleaned.
Data has become an important part of many businesses in the past few decades. It is valuable to companies because it helps them make decisions about their products and services. However, data can also be expensive to collect and maintain over time because of its complexity and volume.
Data is a collection of information that is organized for analysis. Data can be collected through surveys, interviews, or observations. Data can also be collected through the use of technology such as computers and smartphones.
Data is often used to develop strategies and make decisions. It can be used to inform people about the world around them, provide insight for research and development, and predict trends in the future. Data can be analyzed using statistics to better understand what it means and how it relates to other data points.
If the quality of your data is poor, any analysis based on it will be inaccurate as well. Even if you follow every other step of the data analytics process to the letter, it won’t make a difference if your data is a mess.
As a result, the significance of data cleaning cannot be overstated. It’s similar to laying a foundation for a building: do it right, and you’ll be able to construct something strong and long-lasting. If you do it incorrectly, your structure will eventually fall apart.
What is Data cleaning?
Data cleaning is the process of reviewing, sorting, and correcting data. Data cleaning is a tedious job and it requires a lot of attention to detail.
Data cleaning is the process of detecting and correcting errors in a dataset, to it for analysis. This includes fixing errors like typos, formatting inconsistencies, missing values, and incorrect data types. Data cleaning can be done manually or by software
Benefits of data cleaning
Having clean data will enhance overall productivity and help you to make decisions based on the best quality information available. The following are some of the benefits:
- Errors are eliminated when multiple data sources are involved.
- Clients will be happier and staff will be less dissatisfied if there are fewer mistakes.
- Ability to layout the various functions and what your data is supposed to do.
- Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
- Using tools for data cleaning will make for more efficient business practices and quicker decision-making
How do you clean data?
While data cleaning processes differ depending on the sorts of data your company keeps, you may utilize these fundamental steps to create a framework for your company.
- Step 1: Remove duplicate or irrelevant observations
- Step 2: Fix structural errors
- Step 3: Filter unwanted outliers
- Step 4: Handle missing data
- Step 5: Validate and QA